Industrial data analytics in a cloud platform

ABSTRACT

Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets&#39; optimal performance and business goals of the owner&#39;s industrial enterprise, as well as perform real-time decision making and control.

RELATED APPLICATIONS

This application is a continuation of, and claims priority to, U.S.patent application Ser. No. 15/214,583, filed on Jul. 20, 2016, andentitled INDUSTRIAL DATA ANALYTICS IN A CLOUD PLATFORM, which is acontinuation of U.S. patent application Ser. No. 14/087,873, filed onNov. 22, 2013 (issued as U.S. Pat. No. 9,438,648 on Sep. 6, 2016), whichclaims the benefit of U.S. Provisional Patent Application Ser. No.61/821,639, filed on May 9, 2013, and entitled “REMOTE SERVICES ANDASSET MANAGEMENT SYSTEMS AND METHODS.” The entireties of these relatedapplications are incorporated herein by reference.

TECHNICAL FIELD

The subject application relates generally to industrial automation, and,more particularly, to techniques for providing industrial data to acloud platform for analysis by cloud-based applications and services.

BACKGROUND

Industrial controllers and their associated I/O devices are central tothe operation of modern automation systems. These controllers interactwith field devices on the plant floor to control automated processesrelating to such objectives as product manufacture, material handling,batch processing, supervisory control, and other such applications.Industrial controllers store and execute user-defined control programsto effect decision-making in connection with the controlled process.Such programs can include, but are not limited to, ladder logic,sequential function charts, function block diagrams, structured text, orother such programming structures. In general, industrial controllersread input data from sensors and metering devices that provide discreetand telemetric data regarding one or more states of the controlledsystem, and generate control outputs based on these inputs in accordancewith the user-defined program.

In addition to industrial controllers and their associated I/O devices,some industrial automation systems may also include low-level controlsystems, such as vision systems, barcode marking systems, variablefrequency drives, industrial robots, and the like which perform localcontrol of portions of the industrial process, or which have their ownlocalized control systems.

A given industrial enterprise may comprise a large number industrialassets distributed across multiple facilities. These assets may compriseone or more industrial devices operating in conjunction to carry outrespective industrial applications (e.g., batch processing, materialhandling, automated product assembly, quality checks, die casting,etc.). The large number of industrial assets that can make up anindustrial enterprise, together with the often continuous operation ofthose assets, results in generation of a vast amount of potentiallyuseful data across the enterprise. In addition to production statistics,data relating to machine health, alarm statuses, operator feedback(e.g., manually entered reason codes associated with a downtimecondition), electrical or mechanical load over time, and the like areoften monitored, and in some cases recorded, on a continuous basis. Thisdata is generated by the many industrial devices that can make up agiven automation system, including the industrial controller and itsassociated I/O, telemetry devices for near real-time metering, motioncontrol devices (e.g., drives for controlling the motors that make up amotion system), visualization applications, lot traceability systems(e.g., barcode tracking), etc. Moreover, since many industrialfacilities operate on a 24-hour basis, their associated automationsystems can generate a vast amount of potentially useful data at highrates. For an enterprise with multiple plant facilities, the amount ofgenerated automation data further increases.

Collective analysis of enterprise-wide data collected from multipleproduction areas and industrial facilities of an industrial enterprisecould yield useful insights into overall plant operations. However,access to the industrial data is typically limited to applications anddevices that share a common network with the industrial controllers thatcollect and generate the data. As such, plant personnel wishing toleverage the industrial data generated by their systems in anotherapplication (e.g., a reporting or analysis tool, notification system,visualization application, backup data storage, etc.) are required tomaintain such applications on-site using local resources. Moreover,although a given industrial enterprise may comprise multiple plantfacilities at geographically diverse locations (or multiple mobilesystems having variable locations), the scope of such applications islimited only to data available on controllers residing on the same localnetwork as the application.

Also, given the disparate nature of the industrial devices in use at agiven industrial facility, data generated by these devices may conformto several different data types and formats which may not be mutuallycompatible, rendering collective analysis difficult.

The above-described deficiencies of today's industrial control systemsare merely intended to provide an overview of some of the problems ofconventional systems, and are not intended to be exhaustive. Otherproblems with conventional systems and corresponding benefits of thevarious non-limiting embodiments described herein may become furtherapparent upon review of the following description.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is intended to identify key/critical elements orto delineate the scope of the various aspects described herein. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

One or more embodiments of the present disclosure relate to migration ofindustrial data to a cloud platform for collective analysis and devicemanagement. To this end, systems and methods are provided for migratinga customer's industrial data from all stages of manufacturing, as wellas throughout the supply chain, to a cloud platform for collective bigdata analysis. In one or more embodiments, cloud gateway devices cancollect data from the customer's industrial assets, associate the datawith a customer profile, and push the modified data to the cloud foranalysis. The cloud gateways can comprise stand-alone gateway devices,can be integrated into the industrial devices themselves, or can beintegrated in network infrastructure devices on the plant network.Analytics services executing on the cloud platform can receive and storethe data in a customer data store associated with the customer profile.

To facilitate collective analysis of industrial data from multipledisparate sources (e.g., different customers, devices, supply chainentities, and industries), one or more embodiments can facilitatenormalization of some or all of the gathered industrial data so thatdependencies and correlations across data sets can be identified. Forexample, automation devices or cloud gateways can normalize theindustrial data according to a common standard and format prior tomoving the data to the cloud. In other embodiments, data normalizationcan be performed on the cloud side by the analytics service aftermigration of the data to the cloud platform.

Once the industrial data has been moved to the cloud platform,normalized, and associated with a customer profile, analyticsapplications executing on the cloud platform can leverage the data toprovide a number of remote services to the customer. In an exampleapplication, cloud-based services can analyze the data using any of anumber of techniques (e.g., machine learning, data mining, etc.) toidentify dependencies or correlations between aspects of an industrialsystem that may otherwise be hidden to the customer. Cloud-basedanalysis can also identify system trends indicative of an impendingsystem failure or operating inefficiency. In another example, devicemanagement services can compare the device profile for a given device inservice at a customer facility with a product resource data store todetermine whether a newer version of the device or associated softwareis available. Notification services on the cloud platform can deliverupgrade opportunity notifications to specified client devices associatedwith the industrial enterprise. In another example, data collected froma customer facility can be correlated with data collected from similarindustrial applications in use at other customer sites, so thatpredictions and configuration recommendations can be generated based onlearned correlations between asset configurations and systemperformance.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level overview of an industrial enterprise thatleverages cloud-based services.

FIG. 2 is a block diagram of an example cloud-capable industrial device.

FIG. 3 illustrates an exemplary cloud-capable industrial controllerconfigured to process and migrate industrial data to a cloud-platform.

FIG. 4 is a block diagram of an exemplary transformation component.

FIG. 5 illustrates an example context component for transforming rawdata into contextualized data.

FIG. 6 illustrates a configuration in which an industrial device acts asa cloud proxy for other industrial devices comprising an industrialsystem.

FIG. 7 illustrates a configuration in which a firewall box serves as acloud proxy for a set of industrial devices.

FIG. 8 illustrates collection of customer-specific industrial data in acloud platform for cloud-based analysis.

FIG. 9 illustrates a hierarchical relationship between these exampledata classes.

FIG. 10 illustrates delivery of an example device model to a cloudplatform.

FIG. 11 illustrates collection of industrial data into cloud-based BigData for Manufacturing (BDFM) data storage for analysis.

FIG. 12 illustrates a system for normalizing industrial data collectedfrom multiple data sources for collective analysis in a cloud platform.

FIG. 13 illustrates an example data processing technique implemented bycloud-based analytics services to facilitate industry-specific andapplication-specific trend analysis.

FIG. 14 illustrates a cloud-based system for providing industrialanalysis services.

FIG. 15 illustrates generation of a system assessment report based oncomparative analysis of customer-specific data with multi-enterprise ona cloud platform.

FIG. 16 illustrates a cloud-based system for generating automatednotifications of device upgrade opportunities.

FIG. 17 illustrates automatic integration of a cloud-aware smart devicewithin a larger device hierarchy.

FIG. 18 illustrates an example cloud-based architecture for collectingproduct data through an industrial supply chain and identifyingcorrelations and relationships across the supply-chain.

FIG. 19 is a flowchart of an example methodology for sending data froman industrial device to a cloud platform for cloud-based analysis.

FIG. 20 is a flowchart of an example methodology for performingcollecting analysis on industrial data collected from multiple devicesacross multiple industrial facilities.

FIG. 21 is a flowchart of an example methodology for providing deviceand customer information to a cloud platform for use by cloud-basedservices.

FIG. 22 is a flowchart of an example methodology for providing devicemanagement services using cloud-based services.

FIG. 23 is a flowchart of an example methodology for generating assetconfiguration recommendations or notifications based on cloud-basedcomparative analysis with multi-enterprise data.

FIG. 24 is an example computing environment.

FIG. 25 is an example networking environment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding thereof. It may be evident, however, that the subjectdisclosure can be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate a description thereof.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “controller,” “terminal,” “station,” “node,”“interface” are intended to refer to a computer-related entity or anentity related to, or that is part of, an operational apparatus with oneor more specific functionalities, wherein such entities can be eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical or magnetic storage medium)including affixed (e.g., screwed or bolted) or removably affixedsolid-state storage drives; an object; an executable; a thread ofexecution; a computer-executable program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers. Also,components as described herein can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry which is operated by asoftware or a firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that provides at least in part the functionality ofthe electronic components. As further yet another example, interface(s)can include input/output (I/O) components as well as associatedprocessor, application, or Application Programming Interface (API)components. While the foregoing examples are directed to aspects of acomponent, the exemplified aspects or features also apply to a system,platform, interface, layer, controller, terminal, and the like.

As used herein, the terms “to infer” and “inference” refer generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of controllers includes one or more controllers; aset of data resources includes one or more data resources; etc.Likewise, the term “group” as utilized herein refers to a collection ofone or more entities; e.g., a group of nodes refers to one or morenodes.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

To provide a general context for the cloud-based predictive maintenancesystem and services described herein, FIG. 1 illustrates a high-leveloverview of an industrial enterprise that leverages cloud-basedservices. The enterprise comprises one or more industrial facilities104, each having a number of industrial devices 108 and 110 in use. Theindustrial devices 108 and 110 can make up one or more automationsystems operating within the respective facilities 104. Exemplaryautomation systems can include, but are not limited to, batch controlsystems (e.g., mixing systems), continuous control systems (e.g., PIDcontrol systems), or discrete control systems. While some aspects ofthis disclosure are described with reference to either discrete orprocess control applications, it is to be understood that the examplesdescribed herein are not limited to either discrete or process controlindustries or operations. Industrial devices 108 and 110 can includesuch devices as industrial controllers (e.g., programmable logiccontrollers or other types of programmable automation controllers);field devices such as sensors and meters; motor drives; human-machineinterfaces (HMIs); industrial robots, barcode markers and readers;vision system devices (e.g., vision cameras); smart welders; or othersuch industrial devices.

Exemplary automation systems can include one or more industrialcontrollers that facilitate monitoring and control of their respectiveprocesses. The controllers exchange data with the field devices usingnative hardwired I/O or via a plant network such as Ethernet/IP, DataHighway Plus, ControlNet, Devicenet, or the like. A given controllertypically receives any combination of digital or analog signals from thefield devices indicating a current state of the devices and theirassociated processes (e.g., temperature, position, part presence orabsence, fluid level, etc.), and executes a user-defined control programthat performs automated decision-making for the controlled processesbased on the received signals. The controller then outputs appropriatedigital and/or analog control signaling to the field devices inaccordance with the decisions made by the control program. These outputscan include device actuation signals, temperature or position controlsignals, operational commands to a machining or material handling robot,mixer control signals, motion control signals, and the like. The controlprogram can comprise any suitable type of code used to process inputsignals read into the controller and to control output signals generatedby the controller, including but not limited to ladder logic, sequentialfunction charts, function block diagrams, structured text, or other suchplatforms.

Although the exemplary overview illustrated in FIG. 1 depicts theindustrial devices 108 and 110 as residing in fixed-location industrialfacilities 104, the industrial devices may also be part of a mobilecontrol and/or monitoring application, such as a system contained in atransportation unit (e.g., a truck or other service vehicle) or in amobile industrial facility. In another example, industrial devices 108and 110 may comprise devices that perform no control or monitoring of anindustrial system, but instead function only to feed data to thecloud-based analysis system (e.g., a mobile weather station).

According to one or more embodiments of this disclosure, industrialdevices 108 and 110 can be coupled to a cloud platform 102 to leveragecloud-based applications and services. That is, the industrial devices108 and 110 can be configured to discover and interact with cloud-basedcomputing services 112 hosted by cloud platform 102. Cloud platform 102can be any infrastructure that allows shared computing services 112 tobe accessed and utilized by cloud-capable devices. Cloud platform 102can be a public cloud accessible via the Internet by devices havingInternet connectivity and appropriate authorizations to utilize theservices 112. In some scenarios, cloud platform 102 can be provided by acloud provider as a platform-as-a-service (PaaS), and the services 112can reside and execute on the cloud platform 102 as a cloud-basedservice. In some such configurations, access to the cloud platform 102and associated services 112 can be provided to customers as asubscription service by an owner of the services 112. Alternatively,cloud platform 102 can be a private cloud operated internally by theenterprise. An exemplary private cloud platform can comprise a set ofservers hosting the cloud services 112 and residing on a corporatenetwork protected by a firewall.

Cloud services 112 can include, but are not limited to, data storage,data analysis, control applications (e.g., applications that cangenerate and deliver control instructions to industrial devices 108 and110 based on analysis of near real-time system data or other factors),remote monitoring and support, device management, asset performancemanagement, risk assessment services, predictive maintenance services,enterprise manufacturing intelligence services, supply chain performancemanagement, virtualization of the customer's plant environment,notification services, or other such applications. If cloud platform 102is a web-based cloud, industrial devices 108 and 110 at the respectiveindustrial facilities 104 may interact with cloud services 112 via theInternet. In an exemplary configuration, industrial devices 108 and 110may access the cloud services 112 through separate cloud gateways 106 atthe respective industrial facilities 104, where the industrial devices108 and 110 connect to the cloud gateways 106 through a physical orwireless local area network or radio link. In another exemplaryconfiguration, the industrial devices 108 and 110 may access the cloudplatform directly using an integrated cloud gateway service. Cloudgateways 106 may also comprise an integrated component of a networkinfrastructure device, such as a firewall box, router, or switch.

Providing industrial devices with cloud capability via cloud gateways106 can offer a number of advantages particular to industrialautomation. For one, cloud-based storage offered by the cloud platform102 can be easily scaled to accommodate the large quantities of datagenerated daily by an industrial enterprise. Moreover, multipleindustrial facilities at different geographical locations can migratetheir respective automation data to the cloud platform 102 foraggregation, collation, collective big data analysis, andenterprise-level reporting without the need to establish a privatenetwork between the facilities. Industrial devices 108 and 110 and/orcloud gateways 106 having smart configuration capability can beconfigured to automatically detect and communicate with the cloudplatform 102 upon installation at any facility, simplifying integrationwith existing cloud-based data storage, analysis, or reportingapplications used by the enterprise. In another exemplary application,cloud-based diagnostic applications can access the industrial devices108 and 110 via cloud gateways 106 to monitor the health and/orperformance of respective automation systems or their associatedindustrial devices across an entire plant, or across multiple industrialfacilities that make up an enterprise. In another example, cloud-basedlot control applications can be used to track a unit of product throughits stages of production and collect production data for each unit as itpasses through each stage (e.g., barcode identifier, productionstatistics for each stage of production, quality test data, abnormalflags, etc.). These industrial cloud-computing applications are onlyintended to be exemplary, and the systems and methods described hereinare not limited to these particular applications. As these examplesdemonstrate, the cloud platform 102, working with cloud gateways 106,can allow builders of industrial applications to provide scalablesolutions as a service, removing the burden of maintenance, upgrading,and backup of the underlying infrastructure and framework.

FIG. 2 is a block diagram of an example cloud-capable industrial deviceaccording to one or more embodiments of this disclosure. Aspects of thesystems, apparatuses, or processes explained in this disclosure canconstitute machine-executable components embodied within machine(s),e.g., embodied in one or more computer-readable mediums (or media)associated with one or more machines. Such components, when executed byone or more machines, e.g., computer(s), computing device(s), automationdevice(s), virtual machine(s), etc., can cause the machine(s) to performthe operations described.

Cloud-capable industrial device 202 can include a user interfacecomponent 204, a transformation component 206, a customer profilecomponent 208, a cloud gateway component 210, one or more processors212, and memory 214. In various embodiments, one or more of the userinterface component 204, transformation component 206, customer profilecomponent 208, cloud gateway component 210, the one or more processors212, and memory 214 can be electrically and/or communicatively coupledto one another to perform one or more of the functions of thecloud-capable industrial device 202. In some embodiments, components204, 206, 208, and 210 can comprise software instructions stored onmemory 214 and executed by processor(s) 212. Cloud-capable device 202may also interact with other hardware and/or software components notdepicted in FIG. 2. For example, processor(s) 212 may interact with oneor more external user interface devices, such as a keyboard, a mouse, adisplay monitor, a touchscreen, or other such interface devices.

User interface component 204 can be configured to receive user input andto render output to the user in any suitable format (e.g., visual,audio, tactile, etc.). User input can comprise, for example,configuration information defining whether the cloud-aware industrialdevice 202 is allowed to push data to and/or pull data from a cloudplatform. User input can also comprise address information for aparticular cloud platform or a cloud-based application with which thecloud-aware industrial device 202 is to communicate. Transformationcomponent 206 can be configured to transform generated or collectedindustrial data prior to sending the data to the cloud platform. Thiscan include, for example, normalizing the data in accordance withrequirements of a data analytics application on the cloud-platform.Transformation component 206 can also append contextual information tothe industrial data prior to migration. Such contextual information maybe leveraged by cloud-based analytics applications to identifyrelationships, correlations, and dependencies across the customer'sindustrial enterprise (e.g., using machine learning, data mining, orother analytical tools). Other transformations can include datacompression, aggregation, filtering, encryption, or other such datatransformations. In some embodiments, transformation component 206 cantransform the data in accordance with defined a transform profileassociated with the cloud-aware industrial device, which can beconfigured using input received via the user interface component 204.

Customer profile component 208 can be configured to associate items ofindustrial data with customer profile information maintained in thecloud-aware industrial device 202 prior to migrating the data to thecloud platform. Customer profile data can include, for example, a uniquecustomer identifier, contact information (e.g., email addresses, phonenumbers, etc.), or other relevant customer information. Cloud gatewaycomponent 210 can be configured to couple the cloud-aware industrialdevice 202 to a web-based or private cloud. In one or more embodiments,the cloud gateway component 210 can be configured to automaticallyprovide identification and context information relating to itsassociated industrial device upon connection to the cloud, where atleast a portion of the identification and context information isprovided by the customer profile component 208 and transformationcomponent 206, respectively. This information may be used by somecloud-based applications to facilitate integrating the industrial deviceand its associated data with the larger plant-level or enterprise-levelsystem. Cloud gateway component 210 can employ any suitable Internetsecurity protocol to ensure that sensitive data, including informationthat associates the customer's identity with unique process data, ismigrated to the cloud securely.

The one or more processors 212 can perform one or more of the functionsdescribed herein with reference to the systems and/or methods disclosed.Memory 214 can be a computer-readable storage medium storingcomputer-executable instructions and/or information for performing thefunctions described herein with reference to the systems and/or methodsdisclosed. In some embodiments, memory can maintain one or more of adevice profile and a customer profile. The device profile can compriseinformation characterizing the cloud-capable industrial device 202(e.g., model number, device type, current firmware revision, etc.). Thecustomer profile can comprise customer-specific information such as acustomer identifier, customer contact information, a type of industrythat is the focus of the customer's enterprise, etc.

Data migration techniques described herein can be embodied onsubstantially any type of industrial device, including but not limitedto industrial controllers, variable frequency drives (VFDs),human-machine interface (HMI) terminals, telemetry devices, industrialrobots, or other such devices. FIG. 3 illustrates an exemplarycloud-capable industrial controller with the capability to process andmigrate industrial data to a cloud-platform. Industrial controller 302can be, for example, a programmable logic controller (PLC) or other typeof programmable automation controllers (PAC) executing control program312 to facilitate monitoring and control of one or more controlledindustrial processes. Control program 312 can be any suitable code usedto process input signals read into the controller 302 and to controloutput signals from the controller 302, including but not limited toladder logic, sequential function charts, function block diagrams, orstructured text. Data read into or generated by controller 302 can bestored in memory addresses within controller memory (e.g., native memoryor removable storage media).

Industrial controller 302 can exchange data with the controlledindustrial processes through I/O 310, which can comprise one or morelocal or remote input and/or output modules that communicate with one ormore field devices to effect control of the controlled processes. Theinput and/or output modules can include digital modules that send andreceive discrete voltage signals to and from the field devices, oranalog modules that transmit and receive analog voltage or currentsignals to and from the devices. The input and/or output modules cancommunicate with the controller processor over a backplane or networksuch that the digital and analog signals are read into and controlled bythe control program 312. Industrial controller 302 can also communicatewith field devices over a network using, for example, a communicationmodule or an integrated networking port. Exemplary networks over whichcontroller 302 can communicate with field devices can include theInternet, Intranets, Ethernet, Ethernet/IP, DeviceNet, ControlNet, DataHighway and Data Highway Plus (DH/DH+), Remote I/O, Fieldbus, Modbus,Profibus, wireless networks, serial protocols, and the like. It is to beappreciated that industrial controller 302 is not limited to the abovespecifications, and can be any suitable controller used to control anindustrial process.

During operation, industrial controller 302 generates or collects nearreal-time data relating to the controlled processes, such as partcounts, temperatures, motor speeds or loads, vibration data, weights,quality test results, alarms, machine states, operator feedback, orother such information. Some of this data is read by the industrialcontroller 302 directly from field devices (e.g., telemetry devices)associated with the processes themselves, while some data can begenerated by control program 312 based on measured process values. Thedata collected or generated by industrial controller data—raw data320—can be stored in non-volatile memory associated with the industrialcontroller 302, or may only exist on a transient basis (e.g., nearreal-time machine state data that only exists within the controller 302as long as the machine remains in the indicated state, but is not storedin non-volatile memory).

Industrial controller 302 is configured to be cloud-aware, allowing itto connect to a web-based or private cloud platform and utilizecloud-based services hosted thereon (e.g., data storage, analysis,processing, etc.). To this end, industrial controller 302 can include acloud gateway component 318 that couples the industrial controller 302to the cloud. Cloud gateway component 318 can be configured to accessthe cloud through any suitable hardwired or wireless connection to theInternet (e.g., through a network connection to an Internet server, orthrough cloud gateway 106 of FIG. 1). In one or more embodiments, cloudgateway component 318 can execute an auto-configuration routine thatfacilitates connection of industrial controller 302 to the cloud. Inaccordance with this auto-configuration routine, the cloud gatewaycomponent 318 can provide information to the cloud services about theindustrial controller 302, the owner of the industrial controller, andoptionally, a context of the industrial controller 302 within theoverall enterprise or plant hierarchy.

In order to place the raw industrial data in a format suitable forstorage and analysis on the cloud platform, cloud-aware industrialcontroller 302 can include a transformation component 314 configured tomodify or enhance raw data 320 generated or collected by the industrialcontroller 302 into transformed data 322 prior to sending the data tothe cloud. In accordance with determined requirements of the cloudapplications that will be processing the industrial data (e.g.,predictive maintenance applications, risk assessment applications,cloud-based simulation applications, etc.), transformation component 314can filter, prune, re-format, aggregate, summarize, or compress the rawdata 320 to yield transformed data 322. In one or more embodiments, thetransformation component 314 can modify the raw data 320 based on anexplicit or inferred requirement of the cloud application, user-definedtransform profiles instructing how various categories of raw data are tobe transformed prior to being pushed to the cloud, and/or contextualmetadata that provides context for the raw data.

Turning briefly to FIG. 4, a block diagram of an exemplarytransformation component 314 is illustrated. As discussed above,transformation component 314 can be incorporated in an industrialdevice, such as an industrial controller, meter, sensor, motor drive,robot, or other such industrial devices. Transformation component 314can receive raw data 320 generated or collected by the host industrialdevice, and modify the raw data 320 into transformed data 322 that isbetter suited to cloud computing applications.

To this end, transformation component 314 can include one or more of anormalization component 402, a context component 404, an encryptioncomponent 406, a filter component 408, an aggregation component 410, ora compression component 412. Normalization component 402 can beconfigured to convert any specified subset of raw data 320 from a firstformat to a second format in accordance with a requirement of thecloud-based application, thereby normalizing the data for collectiveanalysis with data from other disparate data sources. For example, acloud-based predictive maintenance application may require measuredvalues in a particular common format so that dependencies andcorrelations between different data sets from disparate industrialassets can be identified and analyzed. Accordingly, normalizationcomponent 402 can convert a selected subset of the raw data 320 from anative format to the required common format prior to pushing the data tothe cloud-based predictive maintenance application. Normalizing the rawdata at the industrial device before uploading to the cloud, rather thanrequiring this transformation to be performed on the cloud, can reducethe amount of processing load on the cloud side. However, in someembodiments, the data may be normalized by the cloud-based applicationitself, rather than by the normalization component 402 on the industrialdevice. Such embodiments may reduce the processing load on theindustrial devices themselves, which may be preferable for highlycritical control applications.

Context component 404 can append contextual metadata to the raw data,thereby providing the cloud-based services with useful contextinformation for the industrial data. Context metadata can include, butis not limited to, a time/date stamp, a quality value, a locationassociated with the data (e.g., a geographical location, a productionarea, etc.), machine statuses at the time the data was generated, orother such contextual information, which can be used by the cloud-basedapplication in connection with cloud-side analysis. Turning briefly toFIG. 5, an exemplary context component for transforming raw data intocontextualized data is illustrated. Context component 404 receives rawdata 320 and modifies or enhances the raw data with one or more piecesof context data to yield contextualized data 502. For example, contextcomponent 404 can apply a time stamp to the raw data 320 indicating atime, a date, and/or a production shift when the data was generated. Theapplied context data may also include a production area that yielded thedata, a particular product that was being produced when the data wasgenerated, a state of a machine (e.g., auto, semi-auto, abnormal, etc.)at the time the data was generated, and/or a role of the device withinthe context of the industrial process. Other examples of contextinformation include an employee on shift at the time the data wasgenerated, a lot number with which the data is associated, or an alarmthat was active at the time the data was generated. Context component404 can also apply an actionable data tag to the raw data if it isdetermined that the data requires action to be taken by plant personnelor by the cloud-based application. In yet another example, contextinformation can include data describing behavior of a monitored deviceor machine relative to other related machines, such as timings ofstaggered start sequences or load shedding sequences. In an examplescenario, such data could be used by the cloud-based services to predictor diagnose behavior of a given device or machine based on behavior orconditions of other devices on the same production line.

Context component 404 an also apply contextual information to the rawdata 320 that reflects the data's location within a hierarchicalorganizational model. Such an organization model can represent anindustrial enterprise in terms of multiple hierarchical levels. In anexemplary organizational model, the hierarchical levels can include—fromlowest to highest—a workcell level, a line level, an area level, a sitelevel, and an enterprise level. Devices that are components of a givenautomation system can be described and identified in terms of thesehierarchical levels, allowing a common terminology to be used across theentire enterprise to identify devices, machines, and data within theenterprise. In some embodiments, the organizational model can be knownto the context component 404, which can stamp the raw data 320 with ahierarchical identification tag that indicates the data's origin withinthe organizational hierarchy (e.g.,Company:Marysville:DieCastArea:#1Headline:LeakTestCell).

Returning now to FIG. 4, transformation component 314 may also includean encryption component 406 configured to encrypt sensitive data priorto upload to the cloud. Transformation component 314 can also include afilter component 420 configured to filter the raw data 320 according toany specified filtering criterion (e.g., as defined in a transformprofile associated with the cloud-aware industrial controller 302). Forexample, a transform profile may specify that pressure values below adefined setpoint are to be filtered out prior to uploading the pressurevalues to the cloud. Accordingly, filter component 420 can implementthis requirement by removing pressure valves that fall below thesetpoint prior to moving the data to the cloud.

Aggregation component 410 can be configured to combine related data frommultiple sources. For example, data from multiple sensors measuringrelated aspects of an automation system can be identified and aggregatedinto a single cloud upload packet by aggregation component 410.Compression component 412 can compress data to be uploaded to the cloudusing any suitable data compression algorithm. This can includedetection and deletion of redundant data bits, truncation of precisionbits, or other suitable compression operations.

Transformation component 314 is not limited to the transformationoperations described above in connection with components 402-412, andany suitable data transformation is within the scope of certainembodiments of this disclosure.

Returning now to FIG. 3, the transformed data 322 can be furthermodified by appending data from one or both of a device profile 306 or acustomer profile 308 stored in profile storage 304 of the industrialdevice. Device profile 306 contains information that characterizesindustrial controller 302, including but not limited to a deviceidentifier (e.g., a model number, serial number, vendor identifier,etc.) and a current configuration of the device (e.g., current firmwareversion loaded on the device, current operating system, etc.). Customerprofile 308 contains information identifying the customer (e.g., ownerof industrial controller 302 and associated industrial assets) andrelevant customer-specific information such as personnel contactinformation, the customer's industry, etc. As will be discussed in moredetail below, the customer-specific and device-specific informationcontained in customer profile 308 and device profile 306 can be used bycloud-based services in a number of ways, including but not limited todevice management and version control, alarm notification, etc.

Profiled data 324, comprising the transformed data 322 appended withinformation from device profile 306 and/or customer profile 308, is thensent to the cloud platform by cloud gateway component 318. Servicesresiding on the cloud platform can process the profiled data accordingto defined functionality of the cloud-based services. For example, datacollection services on the cloud-platform can store the receivedindustrial data in customer-specific cloud storage based on the customerprofile data. In another example, cloud-based device management servicescan compare the current device information provided by device profile306 with product information maintained on cloud storage to determinewhether upgrades or software updates are available for the industrialcontroller 302. In yet another example, cloud-based analytics servicescan analyze the received data collectively with data received from otherdevices, assets, and industrial enterprises in order to learn industryspecific trends, provide predictive maintenance notifications, generaterisk assessment reports, identify system configurations or bestpractices that result in superior performance, or other services.

Although FIG. 3 illustrates certain aspects of the present disclosure inconnection with an industrial controller, it is to be appreciated thatthe transformation component 314, customer profile component 316,profile storage 304, and cloud gateway component 318 can be implementedon any suitable industrial device that generates or collects data inconnection with monitoring or controlling an industrial process. Forexample, a variable frequency drive (VFD) used in a motor controlapplication can be provided with cloud interface capabilities so thatmotion control data (e.g., motor speeds, electrical draw, motorpositions, overload conditions, etc.) can be pushed to the cloud forstorage or analysis. Accordingly, such VFDs can include a transformationcomponent that massages and/or contextualizes the data prior touploading the data to the cloud. Similarly, telemetry devices (e.g.,flow meters, weigh scales, voltage meters, pressure meters, sensors,etc.) can be configured to refine their respective metered data forupload to the cloud. Moreover, in some embodiments, an industrial devicecan be configured to serve as a proxy for other industrial devices,wherein the proxy device receives industrial data from other devicescomprising an automation system, transforms the collected data asneeded, and uploads the results to the cloud. In this way, redundantdata collected by multiple devices can be identified and streamlined bythe proxy device prior to pushing the data to the cloud, reducingbandwidth and storage consumption.

As noted above, industrial data can be migrated from industrial devicesto the cloud platform using cloud gateways. To this end, some devicesmay include integrated cloud gateways that directly interface eachdevice to the cloud platform. Alternatively, some configurations mayutilize a cloud proxy device that collects industrial data from multipledevices and sends the data to the cloud platform. Such a cloud proxy cancomprise a dedicated data collection device, such as a proxy server thatshares a network with the industrial devices. Alternatively, the cloudproxy can be a peer industrial device that collects data from otherindustrial devices.

FIGS. 6 and 7 depict example techniques for migrating industrial data tothe cloud platform via proxy devices to facilitate feeding the data to acloud-based big data analyzer. FIG. 6 depicts a configuration in whichan industrial device acts as a cloud proxy for other industrial devicescomprising an industrial system. The industrial system comprises aplurality of industrial devices 606 ₁-606 _(N) which collectivelymonitor and/or control one or more controlled processes 602. Theindustrial devices 606 ₁-606 _(N) respectively generate and/or collectprocess data relating to control of the controlled process(es) 602. Forindustrial controllers such as PLCs or other automation controllers,this can include collecting data from telemetry devices connected to thecontroller's I/O, generating data internally based on measured processvalues, etc.

In the configuration depicted in FIG. 6, industrial device 606 ₁ acts asa proxy for industrial devices 606 ₂-606 _(N), whereby data 614 fromdevices 606 ₂-606 _(N) is sent to the cloud via proxy industrial device606 ₁. Industrial devices 606 ₂-606 _(N) can deliver their data 614 toproxy industrial device 606 ₁ over plant network or backplane 612 (e.g.,a Common Industrial Protocol (CIP) network or other suitable networkprotocol). Using such a configuration, it is only necessary to interfaceone industrial device to the cloud platform (via cloud gateway component608). In some embodiments, cloud gateway component 608 or a separatetransformation component (e.g., transformation component 314) mayperform preprocessing on the gathered data prior to migrating the datato the cloud platform (e.g., time stamping, filtering, formatting,summarizing, compressing, etc.). The collected and processed data canthen be pushed to the cloud platform as cloud data 604 via cloud gatewaycomponent 608. Once migrated, cloud-based data analyzer services canclassify the data according to techniques described in more detailbelow.

While the proxy device illustrated in FIG. 6 is depicted as anindustrial device that itself performs monitoring and/or control of aportion of controlled process(es) 602, other types of devices can alsobe configured to serve as a cloud proxies for multiple industrialdevices according to one or more embodiments of this disclosure. Forexample, FIG. 7 illustrates an embodiment in which a firewall box 712serves as a cloud proxy for a set of industrial devices 706 ₁-706 _(N).Firewall box 712 can act as a network infrastructure device that allowsplant network 716 to access an outside network such as the Internet,while also providing firewall protection that prevents unauthorizedaccess to the plant network 716 from the Internet. In addition to thesefirewall functions, the firewall box 712 can include a cloud gatewaycomponent 708 that interfaces the firewall box 712 with one or morecloud-based services. In a similar manner to proxy industrial device 606₁ of FIG. 6, the firewall box 712 can collect industrial data 714 fromindustrial devices 706 ₁-706 _(N), which monitor and control respectiveportions of controlled process(es) 702. Firewall box 712 can include acloud gateway component 708 that applies appropriate preprocessing tothe gathered industrial data 714 prior to pushing the data tocloud-based analytics systems as cloud data 704. Firewall box 712 canallow industrial devices 706 ₁-706 _(N) to interact with the cloudplatform without directly exposing the industrial devices to theInternet.

Similar to industrial controller 302 described above in connection withFIG. 3, industrial device 606 ₁ or firewall box 712 can tag thecollected industrial data with contextual metadata prior to pushing thedata to the cloud platform (e.g., using a transformation component 314).Such contextual metadata can include, for example, a time stamp, alocation of the device at the time the data was generated, or other suchinformation. In another example, some cloud-aware devices can comprisesmart devices capable of determining their own context within the plantor enterprise environment. Such devices can determine their locationwithin a hierarchical plant context or device topology. Knowledge of thelocation of a given device or machine within the context of the largerplant hierarchy can yield useful insights that can be leveraged forpredictive analysis. For example, an operational demand on a givenmachine on a production line can be anticipated based on an observedincrease in demand on other machines on the same line, based on theknown relationship between the respective machines. Data generated bysuch devices can be normalized to adhere to a hierarchical plant modelthat defines multiple hierarchical levels of an industrial enterprise(e.g., a workcell level, a line level, an area level, a site level, anenterprise level, etc.), such that the data is identified in terms ofthese hierarchical levels. This can allow a common terminology to beused across an entire industrial enterprise to identify devices andtheir associated data. Cloud-based applications and services that modelan enterprise according to such an organizational hierarchy canrepresent industrial controllers, devices, machines, or processes asdata structures (e.g., type instances) within this organizationalhierarchy to provide context for data generated by devices within theenterprise relative to the enterprise as a whole. Such a convention canreplace the flat name structure employed by some industrialapplications.

In some embodiments, cloud gateway components 608 and 708 can compriseuni-directional “data only” gateways that are configured only to movedata from the premises to the cloud platform. Alternatively, cloudgateway components 608 and 708 can comprise bi-directional “data andconfiguration” gateways that are additionally configured to receiveconfiguration or instruction data from services running on the cloudplatform. Some cloud gateways may utilize store-and-forward technologythat allows the gathered industrial data to be temporarily storedlocally on storage associated with the cloud gateway in the event thatcommunication between the gateway and cloud platform is disrupted. Insuch events, the cloud gateways will forward the stored data to thecloud platform when the communication link is re-established.

FIG. 8 illustrates collection of customer-specific industrial data by acloud-based analysis system 814 according to one or more embodiments. Asdiscussed above, analysis system 814 can execute as a cloud-basedservice on a cloud platform (e.g., cloud platform 102 of FIG. 1), andcollect data from multiple industrial systems 816. Industrial systems816 can comprise different industrial automation systems within a givenfacility and/or different industrial facilities at diverse geographicallocations. Industrial systems 816 can also correspond to differentbusiness entities (e.g., different industrial enterprises or customers),such analysis system 814 collects and maintains a distinct customer datastore 802 for each customer or business entity.

Analysis system 814 can organize manufacturing data collected fromindustrial systems 816 according to various classes. In the illustratedexample, manufacturing data is classified according to device data 806,process data 808, asset data 810, and system data 812. FIG. 9illustrates a hierarchical relationship between these example dataclasses. A given plant or supply chain 902 can comprise one or moreindustrial systems 904. Systems 904 represent the production lines orproductions areas within a given plant facility or across multiplefacilities of a supply chain. Each system 904 is made up of a number ofassets 906 representing the machines and equipment that make up thesystem (e.g., the various stages of a production line). In general, eachasset 906 is made up of multiple devices 908, which can include, forexample, the programmable controllers, motor drives, human-machineinterfaces (HMIs), sensors, meters, etc. comprising the asset 906. Thevarious data classes depicted in FIGS. 8 and 9 are only intended to beexemplary, and it is to be appreciated that any organization ofindustrial data classes maintained by analysis system 814 is within thescope of one or more embodiments of this disclosure.

Returning now to FIG. 8, analysis system 814 collects and maintains datafrom the various devices and assets that make up industrial systems 816and classifies the data according to the aforementioned classes for thepurposes of collective analysis. Device data 806 can comprisedevice-level information relating to the identity, configuration, andstatus of the respective devices comprising industrial systems 816,including but not limited to device identifiers, device statuses,current firmware versions, health and diagnostic data, devicedocumentation, identification and relationship of neighboring devicesthat interact with the device, etc.

Process data 808 can comprise information relating to one or moreprocesses or other automation operations carried out by the devices;e.g., device-level and process-level faults and alarms, process variablevalues (speeds, temperatures, pressures, etc.), and the like.

Asset data 810 can comprise information generated collected or inferredbased on data aggregated from multiple industrial devices over time,which can yield a higher asset-level views of industrial systems 816.Example asset data 810 can include performance indicators (KPIs) for therespective assets, asset-level process variables, faults, alarms, etc.Since asset data 810 yields a longer term view of asset characteristicsrelative to the device and process data, analysis system 814 canleverage asset data 810 to identify operational patterns andcorrelations unique to each asset, among other types of analysis. Thesystem can use such patterns and correlations, for example, to identifycommon performance trends as a function of asset configuration fordifferent types of industrial applications.

System data 812 can comprise collected or inferred information generatedbased on data aggregated from multiple assets over time. System data 812can characterize system behavior within a large system of assets,yielding a system-level view of each industrial system 816. System data812 can also document the particular system configurations in use andindustrial operations performed at each industrial system 816. Forexample, system data 812 can document the arrangement of assets,interconnections between devices, the product being manufactured at agiven facility, an industrial process performed by the assets, acategory of industry of each industrial system (e.g., automotive, oiland gas, food and drug, marine, textiles, etc.), or other relevantinformation. Among other functions, this data can be leveraged byanalysis system 814 so that particulars of the customer's unique systemand device configurations can be obtained without reliance on thecustomer to possess complete knowledge of their assets.

As an example, a given industrial facility can include packaging line(the system), which in turn can comprise a number of individual assets(a filler, a labeler, a capper, a palletizer, etc.). Each assetcomprises a number of devices (controllers, variable frequency drives,HMIs, etc.). Using an architecture similar to that depicted in FIG. 1,cloud-based analysis system 814 can collect industrial data from theindividual devices during operation and classify the data in a customerdata store 802 according to the aforementioned classifications. Notethat some data may be duplicated across more than one class. Forexample, a process variable classified under process data 808 may alsobe relevant to the asset-level view of the system represented by assetdata 810. Accordingly, such process variables may be classified underboth classes. Moreover, subsets of data in one classification may bederived or inferred based on data under another classification. Subsetsof system data 812 that characterize certain system behaviors, forexample, may be inferred based on a long-term analysis of data in thelower-level classifications.

In addition to maintaining data classes 806-812, each customer datastore can also maintain a customer model 804 containing data specific toa given industrial entity or customer. Customer model 804 containscustomer-specific information and preferences (at least some of whichmay be received from one or more industrial devices using customerprofile 308), which can be leveraged by analysis system 814 to bettercustomize data analysis and to determine how analysis results should behandled or reported. Example information maintained in customer model804 can include a client identifier, client contact informationspecifying which plant personnel should be notified in response todetection of risk factors (e.g., for embodiments that support real-timerisk assessment monitoring), notification preferences specifying howplant personnel should be notified (e.g., email, mobile phone, textmessage, etc.), preferred data upload frequencies, service contractsthat are active between the customer and a provider of the cloud-basedanalysis services, the customer's industrial concern (e.g., automotive,pharmaceutical, oil and gas, etc.), and other such information.Cloud-based analysis system 814 can marry data collected for eachcustomer with the customer model for identification and event handlingpurposes. In some embodiments, the cloud-based system can serve a custominterface to client devices of authorized plant personnel to facilitateentering or editing the customer model 804. In other embodiments, all orportions of the customer model 804 can be updated in near real-timebased on data maintained on a local server on the plant facility. Forexample, if an engineering manager is replaced, an administrator at theplant facility may update a locally maintained employee database withthe name and contact information for the new manager. The employeedatabase may be communicatively linked to the cloud platform, such thatthe contact information stored in the customer model 804 isautomatically updated to replace the contact information of the outgoingmanager with the new employee contact information.

To ensure a rich and descriptive set of data for analysis purposes,cloud-based analysis services can collect device data in accordance withone or more standardized device models. To this end, a standardizeddevice model can be developed for each industrial device. Device modelsprofile the device data that is available to be collected and maintainedby the cloud-based services. FIG. 10 illustrates an example device modelaccording to one or more embodiments. In the illustrated example, devicemodel 1006 is associated with a cloud-aware industrial device 1002(e.g., a programmable logic controller, a variable frequency drive, ahuman-machine interface, a vision camera, a barcode marking system,etc.). As a cloud-aware device, industrial device 1002 can be configuredto automatically detect and communicate with cloud platform 1008 uponinstallation at a plant facility, simplifying integration with existingcloud-based data storage, analysis, and applications (e.g., the riskassessment system described herein). When added to an existingindustrial automation system, device 1002 can communicate with the cloudplatform and send identification and configuration information in theform of device model 1006 to the cloud platform. Information provided bydevice model 1006 can be maintained in a device profile (e.g., deviceprofile 306 of FIG. 3) stored on the industrial device 1002.

Device model 1006 can be received by a device management component 1014on cloud platform 1008, which then updates the customer's device data806 based on the device model 1006. In this way, cloud based analysissystems can leverage the device model to integrate the new device intothe greater system as a whole. This integration can include updatingcloud-based applications to recognize the new device, adding the newdevice to a dynamically updated data model of the customer's industrialenterprise or plant, making other devices on the plant floor aware ofthe new device, or other such integration functions. Once deployed, somedata items defined by device model 1006 can be collected by cloud-baseddata collection systems, and in some embodiments can be monitored by thecloud-based system on a near real-time basis.

Device model 1006 can comprise such information as a device identifier(e.g., model and serial number), status information for the device, acurrently installed firmware version, device setup data, device warrantyspecifications, calculated and anticipated KPIs associated with thedevice (e.g., mean time between failures), device health and diagnosticinformation, device documentation, or other such parameters.

In addition to maintaining individual customer-specific data stores foreach industrial enterprise, the cloud-based analytics services can alsofeed sets of customer data to a global data storage (referred to hereinas Big Data for Manufacturing, or BDFM, data storage) for collective bigdata analysis in the cloud. As illustrated in FIG. 11, a deviceinterface component 1104 on the cloud platform can collect data fromdevices and assets comprising respective different industrial systems1108 for storage in cloud-based BDFM data storage 1102. In someembodiments, data maintained in BDFM data storage 1102 can be collectedanonymously with the consent of the respective customers. For example,customers may enter into a service agreement with a technical supportentity whereby the customer agrees to have their device and asset datacollected in the cloud platform in exchange for cloud-based analyticsservices (e.g., predictive maintenance services, risk assessmentservices, plant modeling services, real-time asset monitoring, etc.).The data maintained in BDFM data storage 1102 can include all orportions of the classified customer-specific data described inconnection with FIG. 8, as well as additional inferred data. BDFM datastorage 1102 can organize the collected data according to device type,system type, application type, applicable industry, or other relevantcategories. An analysis component 1106 can analyze the resultingmulti-industry, multi-customer data store to learn industry-specific,device-specific, and/or application-specific trends, patterns,thresholds, etc. In general, analysis component 1106 can perform bigdata analysis on the multi-enterprise data maintained BDFM data storageto learn and characterize operational trends or patterns as a functionof industry type, application type, equipment in use, assetconfigurations, device configuration settings, or other such variables.

For example, it may be known that a given industrial asset (e.g., adevice, a configuration of device, a machine, etc.) is used acrossdifferent industries for different types of industrial applications.Accordingly, analysis component 1106 can identify a subset of the globaldata stored in BDFM data storage 1102 relating to the asset or assettype, and perform analysis on this subset of data to determine how theasset or asset type performs over time for each of multiple differentindustries or types of industrial applications. Analysis component 1106may also determine the operational behavior of the asset over time foreach of different sets of operating constraints or parameters (e.g.different ranges of operating temperatures or pressures, differentrecipe ingredients or ingredient types, etc.). By leveraging a largeamount of historical data gathered from many different industrialsystems, analysis component 1106 can learn common operatingcharacteristics of many diverse configurations of industrial assets at ahigh degree of granularity and under many different operating contexts.

Additionally, analysis component 1106 can learn a priori conditions thatoften presage impending operational failures or system degradationsbased on analysis of this global data. The knowledge gleaned throughsuch analysis can be leveraged to detect and identify early warningconditions indicative of future system failures or inefficiencies for agiven customer's industrial system, identify possible modifications tothe customer's industrial assets that may improve performance (e.g.,increase product throughput, reduce downtime occurrences, etc.), orother such services. In some embodiments, analysis component 1106 cancompare operational behavior of similar industrial applications acrossdifferent device hardware platform or software configuration settings,and make a determination regarding which combination of hardware and/orconfiguration settings yield preferred operational performance.Moreover, analysis component 1106 can compare data across differentverticals to determine whether system configurations or methodologiesused at one vertical could beneficially be packaged and implemented foranother vertical. Cloud-based services could use such determinations asthe basis for customer-specific recommendations. In general, BDFM datastorage 1102, together with analysis component 1106, can serve as arepository for knowledge capture and best practices for a wide range ofindustries, industrial applications, and device combinations.

As noted above, some embodiments of the cloud-based analysis systemdescribed herein may require data from different sources to benormalized in order to facilitate collective analysis. To this end,plant floor devices such as industrial controller 302 of FIG. 3 may beconfigured to normalize their generated data to conform to a commonstandard and/or format prior to migrating the data to the cloud.Alternatively, as illustrated in FIG. 12, normalization of the data canbe performed on the cloud platform after migration of the data. In suchembodiments, a normalization component 1202 can receive the collectedmulti-enterprise data from device interface component 1104 and normalizethe data according to the required format before moving the data to BDFMdata storage 1102 and/or customer data store 802.

FIG. 13 illustrates an example data processing technique that can beimplemented by cloud-based analytics services to facilitateindustry-specific and application-specific trend analysis. In thisexample, analysis component 1106 may comprise an industry filter 1302,an application filter 1304, and a grouping component 1306. A user maywish to compare performance metrics for different industrial assetconfigurations that perform a common industrial application (e.g., aparticular pharmaceutical batch process, automotive die casting process,etc.). Since similar sets of industrial devices may be used to carry outsimilar applications in different industries or verticals (e.g., foodand drug, plastics, oil and gas, automotive, etc.), the analytics systemcan identify subsets of BDFM data that were collected from systems inthe relevant industry. This can yield more accurate analysis results,since a given industrial asset configuration—comprising a particular setof industrial devices, firmware revisions, etc.—may demonstratedifferent behavior depending on the industry or vertical in which theasset is used.

Accordingly, industry filter 1302 identifies a subset of the BDFM datacollected from industrial assets in the relevant industry. The relevantsubset of industry-specific data can be identified, for example, basedon information in the customer model 804 associated with each customerdata store 802 (see FIG. 8), which can identify the industry or verticalwith which each customer is associated.

The industry-specific data 1308 comprising the subset of BDFM datarelating to the industry of interest can then be filtered by applicationfilter 1304, which identifies a subset of the industry-specific data1308 relating to a particular industrial application (e.g., a specificbatch process, motor control application, control loop application,barcode tracking application, etc.) within that industry. The resultingapplication-specific data 1310 comprises data (e.g., operational data,abnormal or downtime data, product throughput data, energy consumptiondata, etc.) collected from multiple industrial assets at differentindustrial enterprises that perform the specified industrial applicationwithin the industry of interest. The industrial assets represented byapplication-specific data, while carrying out a common industrialapplication, may comprise different asset configurations (e.g.,different device combinations, different software code, differentfirmware versions or operating systems, etc.).

In order to identify trends or operating characteristics as a functionof the different asset configurations, a grouping component 1306 cansegregate application-specific data 1310 into groups ofconfiguration-specific data 1312. Grouping component 1306 can group thedata according to any suitable asset configuration variable, includingbut not limited to device model, device configuration settings, firmwareversion, software code executed on one or more industrial devicescomprising the industrial assets, or other variable assetcharacteristics. Grouping the application-specific data in this mannerresults in N groups of data, where each group comprises data collectedfrom multiple similarly configured industrial assets/devices thatperform a specific industrial application within a specified industry orvertical. Each group can comprise data from multiple industrialenterprises and customers, so that analysis component 1106 can identifyconfiguration-specific performance trends, propensity for devicefailures, downtime patterns, energy consumption, operating costs, orother such performance metrics as a function of the configuration aspectselected as the grouping criterion.

Filtering and grouping the BDFM data in this manner allows particularasset configurations to be isolated and analyzed individually (e.g.,using machine learning, data mining, or other analysis technique) sothat asset performance trends can be identified for various assetconfigurations. These results can then be compared across the sets ofconfiguration-specific data in order to characterize these learnedperformance metrics as a function of the variable asset configurationscorresponding to the configuration-specific groups. This analysis allowsthe cloud-based analysis system to generate asset configurationrecommendations to specific customers, as will be discussed in moredetail below.

FIG. 14 illustrates a cloud-based system for providing industrialanalysis services. As noted above, cloud-based analysis system 1402 cancollect, maintain, and monitor customer-specific data (e.g. device data806, process data 808, asset data 810, and system data 812) relating toone or more industrial assets 1406 of an industrial enterprise. Inaddition, the analysis system 1402 can collect and organize industrialdata anonymously (with customer consent) from multiple industrialenterprises in BDFM data storage 1102 for collective analysis, asdescribed above in connection with FIG. 11.

Analysis system 1402 can also maintain product resource information incloud-based product resource data storage 1404. In general, productresource data storage 1404 can maintain up-to-date information relatingto specific industrial devices or other vendor products. Product datastored in product resource data storage 1404 can be administered by oneor more product vendors or original equipment manufacturers (OEMs).Exemplary device-specific data maintained by product resource datastorage 1404 can include product serial numbers, most recent firmwarerevisions, preferred device configuration settings and/or software for agiven type of industrial application, or other such vendor-providedinformation.

Additionally, one or more embodiments of cloud-based analysis system1402 can also leverage extrinsic data 1408 collected from sourcesexternal to the customer's industrial enterprise, but which may haverelevance to operation of the customer's industrial systems. Exampleextrinsic data 1408 can include, for example, energy cost data, materialcost and availability data, transportation schedule information fromcompanies that provide product transportation services for the customer,market indicator data, web site traffic statistics, information relatingto known information security breaches or threats, or other suchinformation. Cloud-based analysis system can retrieve extrinsic data1408 from substantially any data source; e.g., servers or other datastorage devices linked to the Internet, cloud-based storage thatmaintains extrinsic data of interest, or other sources.

The system depicted in FIG. 14 can provide analysis services tosubscribing customers (e.g., owners of industrial assets 1406). Forexample, customers may enter an agreement with a product vendor ortechnical support entity to allow their system data to be gathered andfed anonymously into BDFM data storage 1102 (that is, the customer'ssystem data is stored in BDFM storage 1102 without linking the systemdata to the customer's identity), thereby expanding the store of globaldata available for collective analysis. In exchange, the vendor ortechnical support entity can agree to provide customized data analysisservices to the customer (e.g., predictive maintenance notifications,real-time system monitoring, automated email alerting services,automated technical support notification, risk assessment, etc.).Alternatively, the customer may subscribe to one or more available cloudanalytics services, and optionally allow their system data to bemaintained in BDFM data storage 1102. In some embodiments, a customermay be given an option to subscribe to cloud analytics services withoutpermitting their data to be stored in BDFM data storage 1102 forcollective analysis with data from other systems. In such cases, thecustomer's data may only be maintained as customer data (e.g., incustomer data store 802) for the purposes of providing customizedanalysis and notification services, and the collected customer data willbe analyzed in view of BDFM data storage 1102 and product resource datastorage 1404 without being migrated to BDFM data storage for long-termstorage and collective analysis. In another exemplary agreement,customers may be offered a discount on cloud analytics services inexchange for allowing their system data to be anonymously migrated toBDFM data storage 1102 for collective analysis.

Since the cloud-based analysis system 1402 maintains accurate anddetailed documentation of each customer's devices, assets, and systemconfigurations on cloud storage, analysis system 1402 can generatecustomized system configuration recommendations based on comparison ofthe user's system configuration and/or performance data withmulti-enterprise data collected in BDFM data storage 1102. FIG. 15depicts generation of a system assessment report 1502 based oncomparative analysis of customer-specific data 1504 from a customer datastore 802 with multi-enterprise data collected and maintained in BDFMdata storage on the cloud platform. As described above,customer-specific data 1504 can include information characterizing oneor more of the customer's industrial assets, including but not limitedto the industrial devices in use and their functional relationship toone another, the customer's industry, an industrial application beingcarried out by the industrial assets (e.g., a particular batch process,a lot traceability function, an assembly operation, a control loop,etc.), performance metrics measured over time for the assets, etc.

In this example, an analysis component 1106 executing as a service onthe cloud platform analyzes the customer-specific data 1504 in view ofthe groups of configuration-specific data 1312 identified using thetechniques described above in connection with FIG. 13. For example,customer-specific data 1504 may comprise data collected from aparticular industrial asset or system for performing a common industrialapplication within a particular industry (e.g., a batch process in thecustomer's plastics facility). Accordingly, industry filter 1302 canidentify a subset of the multi-enterprise data maintained in BDFM datastorage 1102 collected from plastics industries, and application filter1304 can further filter this industry-specific data to isolate datarelating to the batch process of interest. Grouping component 1306 canthen aggregate subsets of this application-specific data according to aselected configuration aspect (e.g., device models that make up theassets, firmware versions loaded on one or more of the devices, one ormore device parameter settings, etc.). This filtering and groupingprocess results in groups of configuration-specific data 1312, whereeach group corresponds to a different value or setting of the selectedconfiguration aspect.

Analysis component 1106 can then analyze customer-specific data 1504 todetermine which configuration group most closely aligns with thecustomer's particular asset configuration. For example, if theconfiguration-specific data 1312 is grouped according to firmwareversion, such that each group corresponds to a different firmwareversion installed on a particular device, analysis component 1106 candetermine which firmware version is being used by the customer (e.g.,based on the device data maintained on the customer data store 802), andmatch the customer-specific data to the group of configuration-specificdata corresponding to this firmware version. In this way, analysiscomponent 1106 identifies a subset of configuration and performance datacollected from systems that are broadly similar to the customer'sindustrial assets. Leveraging this vast set of multi-enterprise dataallows analysis component 1106 to generate customized predictions andrecommendations relevant to the customer's particular industrial assets.

For example, analysis component 1106 can generate a system assessmentreport 1502 identifying predicted asset performance issues based onanalysis of the configuration-specific data group identified as beinganalogous to the customer's system. In this scenario, analyticscomponent may infer, based on the relevant configuration-specific datagroup, that devices using the same firmware version in use at thecustomer's facility for similar industrial applications experience arelatively high number of downtime occurrences or compatibility issueswith other devices. Accordingly, system assessment report 1502 canidentify these predicted asset performance and device compatibilityissues as potential concerns. In another example, system assessmentreport 1502 can comprise a risk assessment report that identifies one ormore potential risk factors associated with the customer's system basedon analysis of the relevant configuration-specific data 1312. Such riskfactors can include, for example, risk of device failure or degradedperformance, safety risks, lost revenue opportunities, risks ofinventory shortage, network security risks, etc. For each identifiedrisk factor, the risk assessment report can also include a descriptionof possible effects that realization of the risk factor may have on thecustomer's overall operation, possible financial impact, and/or one ormore risk aversion recommendations for mitigating the risk factor.

By virtue of the configuration-specific data groupings identified by thecloud-based analysis system, analysis component 1106 can also comparethe user's industrial asset configurations with other alternativeconfigurations for implementing the same industrial application (e.g.,configurations corresponding to other configuration-specific datagroups). Continuing with the firmware example described above, analysiscomponent 1106 may determine, based on comparison of performance dataacross the configuration-specific data groups, that systems using adifferent firmware version relative to the version currently installedon the user's system generally experience fewer downtime occurrences,experience greater product throughput, consume less energy, etc. Basedon this observation, system assessment report may include arecommendation to install the preferred firmware version on theappropriate industrial device. System assessment report 1502 can alsorecommend other configuration modifications using similar techniques(e.g., replacement of an existing device to a different device model,modification of existing parameter settings, reconfiguration of aportion of the plant network, etc.).

In another example, configuration-specific data 1312 may be groupedaccording to program code used to execute the industrial application,thereby allowing analysis component to compare performance metrics forthe different groups as a function of program code. Accordingly,customer-specific data 1504 may include information relating to theprogram code in use on the customer's particular system. Based on thisinformation, analysis component 1106 can compare the code in use at thecustomer facility with alternative programs used at other facilities todetermine whether one of these alternative programming may improveperformance of the customer's system. These alternative programs can beidentified in system assessment report 1502.

In addition to the features discussed above, one or more embodiments canprovide cloud-based device management services that assist users withdevice upgrade notification and management. FIG. 16 illustrates acloud-based system for generating automated notifications of deviceupgrade opportunities. In one or more embodiments, users may enter anagreement with a provider of the device management services to allowtheir basic device data to be collected and monitored in thecloud-platform in exchange for automated alerting services. Suchagreements may allow the user (e.g., the owner of the industrial assetsbeing monitored) to select from one or more device management options,including but not limited to firmware upgrade notifications,configuration management recommendations, program upload and compareservices, or other device management services.

Based on the service agreement, one or more devices of the user'sindustrial assets—e.g., cloud-aware smart device 1604—can provide deviceand customer data 1602 to the cloud platform using techniques describedabove. For example, cloud-aware smart device 1604 can be similar toindustrial controller 302 of FIG. 3, such that device 1604 maintains adevice profile 306 and a customer profile 308. Device 1604 can provideat least a portion of the data stored in these profiles to the cloudplatform using cloud gateway component 210. This data can include, butis not limited to, a device identifier (e.g., device model number), acustomer identifier, contact information for the customer (e.g., emailaddresses for plant personnel who should be notified in the event of adetected upgrade opportunity or configuration recommendation),configuration data for the device (e.g., a firmware version, parametersettings, program code, etc.), or other such information.

Device management component 1014 can receive this device and customerdata 1602 in the cloud platform, and cross-reference this data withproduct version data maintained in product resource data storage 1404.For example, device management component 1014 may submit the deviceidentifier received from the smart device 1604 to product resource datastorage 1404 to determine whether a newer version of the device iscurrently available, or of a newer firmware version is available for thedevice. Since product resource data storage 1404 is kept up-to-date withthe most recent product availability information, device managementcomponent 1014 is able compare the user's current device informationwith the product availability information on a near real-time basis, andnotify the user when an upgrade opportunity is detected. If a newerdevice or firmware version is detected, device management component 1014can instruct a notification component 1606 to send a notification to oneor more client devices 1610 specified by the contact informationprovided in device and customer data 1602 (or specified on customermodel 804).

To facilitate accurate system analysis that considers not only thedevices in use at a customer facility, but also the relationshipsbetween the devices and their context within the larger enterprise, someembodiments of the cloud-based analysis system can maintain a plantmodel for a given industrial enterprise based on the data collected asdescribed above. To this end, services executing on the cloud platformcan facilitate automatic integration of new or existing industrialdevices into the plant model. Pursuant to an example, FIG. 17illustrates automatic integration of a cloud-aware smart device within alarger device hierarchy. In this example, a cloud-aware smart device1702 installed as part of an industrial automation system within anindustrial enterprise communicates with a cloud platform through anInternet layer. In some scenarios, cloud gateway component 210 caninitiate communication with the cloud platform upon installation andpower-up of cloud-aware smart device, where the particular cloudplatform or cloud-based application with which the device communicatesis specified in a configuration file associated with the device. Oncecommunication is established, cloud-aware smart device 1702 can beginexchanging cloud data 1704 with the cloud platform. Although cloudgateway component 210 is depicted in FIG. 17 as exchanging cloud data1704 directly with the cloud platform, in some scenarios cloud gatewaycomponent 210 may communicate with the cloud platform via a separatecloud gateway or other proxy device, as described above.

In the present example, cloud-aware smart device 1702 communicates withdevice management component 1014 running on the cloud platform. Devicemanagement component 1014 can maintain a plant model 1708 that modelsthe industrial enterprise and devices therein. Plant model 1708 canrepresent the industrial enterprise in terms of multiple hierarchicallevels, where each level comprises units of the enterprise organized asinstances of types and their properties. Exemplary types can include,for example, assets (e.g., pumps, extruders, tanks, fillers, weldingcells, utility meters, etc.), structures (e.g., production lines,production areas, plants, enterprises, production schedules, operators,etc.), and processes (e.g., quality audit, repairs, test/inspection,batch, product parameters, shifts, etc.).

Plant model 1708 allows devices of an automation system and data itemsstored therein to be described and identified in terms of thesehierarchical levels, allowing a common terminology to be used across theentire enterprise to identify devices and data associated with thosedevices. Thus, individual items of device data (e.g., live analog anddigital values stored in controller tags, archived data values stored ina historian register or other long-term data storage device, etc.), whenintegrated into plant model 1708, can be identified and viewed by otherapplications using unique tags defined by plant model 1708. Plant model1708 can represent industrial controllers, devices, machines, orprocesses as data structures (e.g., type instances) within thisorganizational hierarchy to provide context for data generated andstored throughout the enterprise relative to the enterprise as a whole.

Device management component 1014 can leverage device model informationprovided by cloud-aware smart device 1702 to facilitate auto-discoveryof the smart device and creation of corresponding data structuresrepresenting the smart device in plant model 1708. For example, whencloud-aware smart device 1702 is added to an industrial system of anenterprise, the device's cloud gateway component 210 can sendinformation from device profile 306 to device management component 1014on the cloud platform. Device management component 1014 can therebyautomatically detect the device and determine the device's contextwithin the organizational hierarchy modeled by plant model 1708, andreconfigure plant model 1708 to incorporate the newly added device atthe appropriate location within the organizational hierarchy. This caninclude identifying data tags available within cloud-aware smart deviceand making those data tags available for viewing or retrieval byauthorized applications using the hierarchical naming structure definedby plant model 1708.

In some embodiments, cloud-aware smart device 1702 can generate aportion of the device hierarchy represented by plant model 1708, andprovide this information to the cloud platform to facilitate accuratelyrepresenting this portion of the device hierarchy within plant model1708. For example, if cloud-aware smart device 1702 is an industrialcontroller that monitors and/or controls one or more production areas,the smart industrial controller can generate a hierarchicalrepresentation of the production areas, production lines, workcells,etc. of which it has knowledge, as well as devices associated therewith(e.g., I/O devices or subservient control systems that interface withthe industrial controller). Cloud gateway component 210 can provide thisportion of the enterprise hierarchy to the cloud platform, and devicemanagement component 1014 can update plant model 1708 accordingly. Dataprovided by cloud-aware smart device 1702, together with plant model1708, can be leveraged by any suitable cloud services 1706 residing onthe cloud platform, including the cloud-based analysis servicesdescribed herein.

Since the cloud-based analysis services described herein can associategeographically diverse data with a customer identifier (e.g., customermodel 804) and aggregate this data in a cloud platform, the system cantake advantage of the large amounts of diverse data from all stages of asupply chain to identify factors at one stage that impact quality orperformance elsewhere in the chain. This can include collection andanalysis of data from material or parts suppliers, distributors,inventory, sales, and end-user feedback on the finished product. FIG. 18illustrates an exemplary cloud-based architecture for tracking productdata through an industrial supply chain and identifying correlations andrelationships across the supply-chain. A simplified supply chain caninclude a supplier 1804, a manufacturing facility 1806, a warehouse1808, and a retail entity 1810. However, the supply chain can comprisemore or fewer entities without departing from the scope of thisdisclosure. For simplicity, FIG. 18 depicts a single block for eachsupply chain entity. However, it is to be appreciated that a givensupply chain can comprise multiple entities for each entity type. Forexample, a manufacturing facility may rely on materials provided bymultiple suppliers. Likewise, the supply chain may include multiplewarehouse entities to provide storage for various products produced bythe manufacturing facility, shared distribution centers, and multipleretail entities for selling the products to end customers.

The various supply chain entities can generate a large amount of data inconnection with their roles in the supply chain. For example, supplier1804 and manufacturing facility 1806 can include plant floor devicesthat generate near real-time and historical industrial data relating toproduction of the materials or products, as well as business-levelinformation relating to purchase orders, intakes, shipments, enterpriseresource planning (ERP), and the like. Warehouse 1808 can maintainrecords of incoming and outgoing product, product quality constraintssuch as storage temperatures, and current inventory levels forrespective products. Retail entity 1810 can track sales, retailinventory, lost or damaged product, financial information, demandmetrics, and other such information. Additional information relating totransportation of materials or products between stages of the supplychain can also be generated, including but not limited to geographicallocation obtained from global positioning systems.

According to one or more embodiments, data sources associated with eachof the supply chain entities can provide industrial or business data tocloud platform 1802 to facilitate cloud-based tracking of productsthrough the supply chain and prediction of potential quality issues.Cloud platform 1802 can execute a number of services that aggregate andcorrelate data provided by the various supply chain stages, and provideinformation about a product's state within the supply chain based on theanalysis. These cloud-based services can include, but are not limitedto, tracking the product's physical location within the supply chain,providing metrics relating to the flow of products through the supplychain, or identifying and troubleshooting current and predictedinefficiencies in product flows through the supply chain.

In a non-limiting example, cloud-based services 1812 may note a spike innegative feedback from purchasers of the end product (e.g., based onsurvey data collected from retail entity 1810). Using analytics similarto those described in previous examples, cloud-based analysis servicescan trace the cause of the reported quality issue to changes made to anupstream process of the supply chain, such as a new material supplier1804 providing an inferior ingredient, an equipment upgrade at themanufacturing facility 1806 that may have had an impact on productquality, or other such factor. Analysis at the supply-chain level caninvolve analysis over longer durations than those involve forplant-level or batch-level troubleshooting, since supply-chaincharacteristics are characterized by data collected throughout thesupply chain workflow.

In addition to the analytics features described above, collection of acustomer's device, asset, process, and system data in the cloud platformin association with a customer model establishes a framework for othertypes of services. For example, a cloud-based advertising system cangenerate target advertisements based on a customer's current devices inservice, known customer preferences stored in the customer model, orother factors obtainable from the customer's data store. Suchadvertisements could direct customers to alternative devices that couldreplace, supplement, or enhance their existing equipment.

Moreover, the volume of customer-specific data and diverse global datagathered and maintained by the cloud-based analytics system can beleveraged to generate reports that offer multi-dimensional views of acustomer's industrial assets and processes. For example, based onanalysis of the data maintained in the customer data stores, thecloud-based services can calculate or anticipate customer-specific KPIsfor a given industrial system or asset. Additionally, reports can begenerated that benchmark these customer-specific KPIs against theglobal, multi-customer data set maintained in the BDFM data store.

As noted above in connection with FIG. 14, the various types ofcloud-side analyses described above can correlate extrinsic data 1408with the customer-specific and multi-enterprise data in order to providerecommendations, alerts, or other information to the customer regardingthe configuration or operation of their industrial assets. In thisregard, cloud-based analysis system can learn relationships betweenextrinsic data 1408 and operation of the customer's industrial system,and identify aspects of the customer's operations that may be a partialfunction of events outside the industrial enterprise or supply chain.For example, extrinsic data 1408 may comprise, in part, current healthstatistics collected from a medical database. The medical database maycomprise a server maintained by a government health organization that ispublicly accessible via the Internet, or may comprise cloud-basedstorage that maintains publically accessible health statistics. Based onanalysis of this extrinsic data, analysis component 1106 may identify anincrease in reported influenza cases for a geographical location withina customer's retail area. Accordingly, the cloud-based analysis systemmay predict an increase in demand for a relevant pharmaceutical productmanufactured by the customer's pharmaceutical enterprise. Based onfurther analysis of collected customer data (e.g., device data 806,process data 808, asset data 810, and/or system data 812), analysiscomponent 1106 may determine that the current inventory level maintainedby the customer may be insufficient to meet the expected elevateddemand, and report this possible inventory shortage via systemassessment report 1502 (which may also include a recommendation toincrease production of the pharmaceutical product to a level expected tosatisfy the anticipated demand).

In another example, extrinsic data 1408 may comprise current energy ormaterial cost information. Analysis component 1106 may factor this costinformation into a cost-benefit analysis, together with the collectedcustomer data, in order to determine whether revenue for the industrialenterprise would be substantially optimized by increasing or loweringthe current rate of production (e.g., scheduling more or fewer shifts,curtailing operations during peak energy demand hours, etc.). In othernon-limiting, industry-specific examples, analysis component 1106 maypredict an increase in demand for certain pet supplies (e.g., pet foodor accessories) based on an observed rise in the number of petadoptions, or may predict a rise in demand for infant products (e.g.,formula, diapers, etc.) based on a detected rise in the number of childbirths (given birth statistics received as extrinsic data 1408).

FIGS. 19-23 illustrate various methodologies in accordance with one ormore embodiments of the subject application. While, for purposes ofsimplicity of explanation, the one or more methodologies shown hereinare shown and described as a series of acts, it is to be understood andappreciated that the subject innovation is not limited by the order ofacts, as some acts may, in accordance therewith, occur in a differentorder and/or concurrently with other acts from that shown and describedherein. For example, those skilled in the art will understand andappreciate that a methodology could alternatively be represented as aseries of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement amethodology in accordance with the innovation. Furthermore, interactiondiagram(s) may represent methodologies, or methods, in accordance withthe subject disclosure when disparate entities enact disparate portionsof the methodologies. Further yet, two or more of the disclosed examplemethods can be implemented in combination with each other, to accomplishone or more features or advantages described herein.

FIG. 19 illustrates an example methodology 1900 for sending data from anindustrial device to a cloud platform for cloud-based analysis.Initially, at 1902, industrial data relating to a controlled industrialprocess is collected or generated. In one or more embodiments, theindustrial data is generated by an industrial device (e.g., anindustrial controller, telemetry device, motor drive, HMI terminal,vision system, etc.) that is communicatively linked to a cloud platformand that subsequently provides the data to the cloud. Alternatively, thedata is collected from multiple industrial devices by a cloud proxydevice (e.g., a stand-alone cloud-aware server, a firewall box or othernetwork infrastructure device, etc.).

At 1904, the industrial data is normalized to conform to a standard orformat required by a big data analyzer that executes as a service on thecloud platform. At 1906, the normalized data is sent to the cloudplatform for storage and/or analysis by the big data analyzer.

FIG. 20 illustrates an example methodology 2000 for performingcollecting analysis on industrial data collected from multiple devicesacross multiple industrial facilities. Initially, at 2002, industrialdata is collected from multiple industrial enterprises in a cloudplatform. This can comprise collecting one or more of device, asset,process, and/or system data from industrial devices and assets acrossthe enterprises. At 2004, the data collected at step 2002 is normalizedin the cloud platform to conform to a standard used by an analysisapplication that executes as a service on the cloud platform. At 2006,collective analysis is performed on the normalized data in the cloudplatform. Such collective analysis can include, for example, learningand identifying asset performance trends as a function of systemconfiguration, predicting impending device failures or operationalinefficiencies, identifying risk factors inherent in certain systemconfigurations, inferring lifecycle data for particular industrialdevices, building interactive models of an industrial system, or othertypes of analysis.

FIG. 21 is an example methodology for providing device and customerinformation to a cloud platform for use by cloud-based services.Initially, at 2102, an industrial device is configured with a deviceprofile and a customer profile. The device profile maintains currentinformation that characterizes the industrial device, including but notlimited to a device identifier (e.g., model number), vendor information,current firmware version, current parameter settings, a context of thedevice within its industrial environment (e.g., other devices that arecommunicatively linked to the industrial device), and/or other suchinformation. The customer profile stores customer-specific informationregarding an owner of the industrial device, including a customeridentifier, contact information for relevant plant personnel (e.g.,email addresses, phone numbers, etc.), a relevant industry that is thefocus of the customer's industrial enterprise (e.g., automotive, oil andgas, food and drug, mining, etc.), and/or other customer-specificinformation.

At 2104, communication is established between the industrial device anda cloud-based application executing as a service on a cloud platform. Inone or more embodiments, the industrial device may include a cloudgateway that communicatively links the industrial device to the cloudplatform. The cloud gateway may be configured to automatically detectthe cloud-based application upon deployment of the device on a plantnetwork, and to establish communication with the application upondetection. At 2106, the industrial device sends information derived fromthe device profile and the customer profile to the cloud-basedapplication via the communicative link established at step 2104.

FIG. 22 is an example methodology for providing device managementservices using cloud-based services. Initially, at 2202, device profileand customer profile information is received at a cloud-basedapplication from an industrial application. The device profile andcustomer profile information can be provided to the cloud platform usingthe methodology depicted in FIG. 21 and described above. At 2204, deviceidentification information retrieved from the device profile informationis cross-referenced with product resource data maintained on cloudstorage of the cloud platform. The product resource data can includeversion and upgrade information for multiple industrial devices (e.g.,available device models, most recent firmware versions, operating systemupdates, etc.).

At 2206, current version information for the industrial devicecorresponding to the device identification information is determinedbased on the cross-referencing of step 2204. At 2208, a determination ismade regarding whether new capabilities are available for the industrialdevice. This can include determining whether a newer version of thedevice is available, whether a more recent firmware version is availablefor the device, whether software upgrades are available, etc. If no newcapabilities are available, the methodology ends. Alternatively, if newcapabilities are identified (“YES” at step 2208), a notification is sentto one or more client devices or addresses defined in the customerprofile information, informing plant personnel of the identifiedcapabilities. For example, the cloud-based device management applicationcan notify a user that a new firmware version is available for thedevice, that a newer version of the device is available, etc. Thenotification may also include instructions for obtaining the newcapabilities (e.g., a website from which the new firmware version can bedownloaded, a vendor from which the new device model can be purchased,etc.).

FIG. 23 illustrates an example methodology 2300 for generating assetconfiguration recommendations or notifications based on cloud-basedcomparative analysis with multi-enterprise data. Initially, at 2302,industrial data is collected in a cloud platform from multipleindustrial enterprises. The industrial data can comprise one or more ofdevice, asset, process, and system data, as described in previousexamples. At 2304, subsets of the collected data comprising performancedata for industrial assets that perform a common industrial applicationare identified. For example, a cloud-based analysis system can identifysubsets of the collected data corresponding to collections of industrialequipment that perform a particular batch process, where the respectivesubsets are collected from multiple industrial assets that perform thebatch process but which comprise different sets of devices, deviceconfigurations, software code, etc. The analysis system may also furtheridentify a subset of this application-specific data corresponding to aparticular industry (e.g., automotive, oil and gas, food and drug,textiles, etc.).

At 2306, the identified data subsets are grouped according to a variableasset configuration. For example, the cloud-based analysis system cangroup together data subsets corresponding to industrial assets thatinclude a particular device model, firmware version, networkconfiguration, configuration parameter, or other variable configurationaspect. In this way, each data group represents performance data for theindustrial application as a function of the variable configurationaspect.

At 2308, the performance data is compared across the groups establishedin step 2306, in order to determine relative performance metrics for thedifferent asset configurations as a function of the variableconfiguration aspect. At 2310, customer-specific asset data collectedfrom a particular customer's industrial enterprise is correlated withthe collected performance data. For example, the customer-specific datacan be cross-referenced with the performance data groups established atstep 2306 in view of the performance comparisons made at step 2308. At2312, one or more asset configuration recommendations are generatedbased on the correlation of step 2310. For example, the cloud-basedanalysis system may determine, based on the customer-specific assetconfiguration data, that the customer's equipment configuration forperforming the industrial application generally corresponds to thevariable configuration aspect for one of the identified groups (step2306), and is therefore expected to experience similar performancecharacteristics identified for that group based on the analysisperformed at step 2308. Accordingly, the analysis system can generate arecommendation that the customer alter the current asset configurationto more closely conform to a configuration associated with a differentdata group that demonstrates improved performance relative to thecustomer's current configuration (e.g., a recommendation to change aparameter setting, a recommendation to use a different firmware version,a recommendation to replace a particular device with a different modelknown or inferred to result in improved performance, etc.). In someembodiments, the analysis system can also provide positive confirmationthat the user's current system configuration generally conforms to thatidentified as yielding best performance.

Embodiments, systems, and components described herein, as well asindustrial control systems and industrial automation environments inwhich various aspects set forth in the subject specification can becarried out, can include computer or network components such as servers,clients, programmable logic controllers (PLCs), automation controllers,communications modules, mobile computers, wireless components, controlcomponents and so forth which are capable of interacting across anetwork. Computers and servers include one or more processors—electronicintegrated circuits that perform logic operations employing electricsignals—configured to execute instructions stored in media such asrandom access memory (RAM), read only memory (ROM), a hard drives, aswell as removable memory devices, which can include memory sticks,memory cards, flash drives, external hard drives, and so on.

Similarly, the term PLC or automation controller as used herein caninclude functionality that can be shared across multiple components,systems, and/or networks. As an example, one or more PLCs or automationcontrollers can communicate and cooperate with various network devicesacross the network. This can include substantially any type of control,communications module, computer, Input/Output (I/O) device, sensor,actuator, and human machine interface (HMI) that communicate via thenetwork, which includes control, automation, and/or public networks. ThePLC or automation controller can also communicate to and control variousother devices such as I/O modules including analog, digital,programmed/intelligent I/O modules, other programmable controllers,communications modules, sensors, actuators, output devices, and thelike.

The network can include public networks such as the internet, intranets,and automation networks such as control and information protocol (CIP)networks including DeviceNet, ControlNet, and Ethernet/IP. Othernetworks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus,Profibus, CAN, wireless networks, serial protocols, and so forth. Inaddition, the network devices can include various possibilities(hardware and/or software components). These include components such asswitches with virtual local area network (VLAN) capability, LANs, WANs,proxies, gateways, routers, firewalls, virtual private network (VPN)devices, servers, clients, computers, configuration tools, monitoringtools, and/or other devices.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 24 and 25 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 24, an example environment 2410 for implementingvarious aspects of the aforementioned subject matter includes a computer2412. The computer 2412 includes a processing unit 2414, a system memory2416, and a system bus 2418. The system bus 2418 couples systemcomponents including, but not limited to, the system memory 2416 to theprocessing unit 2414. The processing unit 2414 can be any of variousavailable processors. Multi-core microprocessors and othermultiprocessor architectures also can be employed as the processing unit2414.

The system bus 2418 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 8-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 2416 includes volatile memory 2420 and nonvolatilememory 2422. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer2412, such as during start-up, is stored in nonvolatile memory 2422. Byway of illustration, and not limitation, nonvolatile memory 2422 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable PROM (EEPROM), or flashmemory. Volatile memory 2420 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM).

Computer 2412 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 24 illustrates, forexample a disk storage 2424. Disk storage 2424 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 2424 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 2424 to the system bus 2418, a removableor non-removable interface is typically used such as interface 2426.

It is to be appreciated that FIG. 24 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 2410. Such software includes an operatingsystem 2428. Operating system 2428, which can be stored on disk storage2024, acts to control and allocate resources of the computer 2412.System applications 2430 take advantage of the management of resourcesby operating system 2428 through program modules 2432 and program data2434 stored either in system memory 2416 or on disk storage 2424. It isto be appreciated that one or more embodiments of the subject disclosurecan be implemented with various operating systems or combinations ofoperating systems.

A user enters commands or information into the computer 2412 throughinput device(s) 2436. Input devices 2436 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 2414through the system bus 2418 via interface port(s) 2438. Interfaceport(s) 2438 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 2440 usesome of the same type of ports as input device(s) 2436. Thus, forexample, a USB port may be used to provide input to computer 2412, andto output information from computer 2412 to an output device 2440.Output adapters 2442 are provided to illustrate that there are someoutput devices 2440 like monitors, speakers, and printers, among otheroutput devices 2440, which require special adapters. The output adapters2442 include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 2440and the system bus 2418. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 2444.

Computer 2412 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)2444. The remote computer(s) 2444 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer2412. For purposes of brevity, only a memory storage device 2446 isillustrated with remote computer(s) 2444. Remote computer(s) 2444 islogically connected to computer 2412 through a network interface 2448and then physically connected via communication connection 2450. Networkinterface 2448 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 2450 refers to the hardware/softwareemployed to connect the network interface 2448 to the system bus 2418.While communication connection 2450 is shown for illustrative clarityinside computer 2412, it can also be external to computer 2412. Thehardware/software necessary for connection to the network interface 2448includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 25 is a schematic block diagram of a sample computing environment2500 with which the disclosed subject matter can interact. The samplecomputing environment 2500 includes one or more client(s) 2502. Theclient(s) 2502 can be hardware and/or software (e.g., threads,processes, computing devices). The sample computing environment 2500also includes one or more server(s) 2504. The server(s) 2504 can also behardware and/or software (e.g., threads, processes, computing devices).The servers 2504 can house threads to perform transformations byemploying one or more embodiments as described herein, for example. Onepossible communication between a client 2502 and servers 2504 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The sample computing environment 2500 includes acommunication framework 2506 that can be employed to facilitatecommunications between the client(s) 2502 and the server(s) 2504. Theclient(s) 2502 are operably connected to one or more client datastore(s) 2508 that can be employed to store information local to theclient(s) 2502. Similarly, the server(s) 2504 are operably connected toone or more server data store(s) 2510 that can be employed to storeinformation local to the servers 2504.

What has been described above includes examples of the subjectinnovation. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe disclosed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the subjectinnovation are possible. Accordingly, the disclosed subject matter isintended to embrace all such alterations, modifications, and variationsthat fall within the spirit and scope of the appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the disclosed subjectmatter. In this regard, it will also be recognized that the disclosedsubject matter includes a system as well as a computer-readable mediumhaving computer-executable instructions for performing the acts and/orevents of the various methods of the disclosed subject matter.

In addition, while a particular feature of the disclosed subject mattermay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes,” and “including” and variants thereof are used ineither the detailed description or the claims, these terms are intendedto be inclusive in a manner similar to the term “comprising.”

In this application, the word “exemplary” is used to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

What is claimed is:
 1. A system for processing industrial data,comprising: a memory that stores executable components; a processor,operatively coupled to the memory, that executes executable components,the executable components comprising: a device interface componentconfigured to receive data from an industrial device of an industrialenterprise, wherein the data comprises at least process data relating toan industrial process appended with a plant site identifier identifyinga plant site at which the industrial device is located, a productionline identifier identifying a production line associated with theindustrial device, and device identification data identifying theindustrial device, and wherein the device interface component is furtherconfigured to store the data in a data storage with other data collectedfrom other industrial from other industrial enterprises; an analysiscomponent configured to filter the data and the other data in the datastorage according to an industry type and a type of industrialapplication defined on a customer model to yield filtered data, performan analysis on the filtered data, and identify at least one of apredicted performance issue of the industrial device or a configurationmodification for improving performance of the industrial device based ona result of the analysis; and a communication component configured tosend notification data identifying at least one of the predictedperformance issue or the configuration modification to a client devicevia the cloud platform.
 2. The system of claim 1, wherein the analysiscomponent is further configured to generate, based on another analysisof the filtered data, comparative performance metrics across differentdevice configurations represented by the filtered data, and thecommunication component is configured to send report data to the clientdevice or another client device rendering the comparative performancemetrics.
 3. The system of claim 1, wherein the analysis component isfurther configured to identify a device compatibility issue between theindustrial device and another industrial device based on the result ofthe analysis, and the communication component is configured to sendreport data to the client device or another client device renderinginformation about the device compatibility issue.
 4. The system of claim1, wherein the device identification data comprises at least one of adevice identifier of the industrial device, a firmware revisionidentifier, a software code identifier, an operating system identifier,a configuration parameter setting for the industrial device, a statusindicator for the industrial device, or a role identifier thatidentifies a role of the industrial device in the industrial process. 5.The system of claim 1, wherein the analysis component is furtherconfigured to identify, based on the result of the analysis, acorrelation between downtime occurrences of machines that perform thetype of industrial application and a device firmware version ofindustrial devices respectively associated with the machines, and thecommunication component is configured to send report data to the clientdevice or another client device identifying the correlation.
 6. Thesystem of claim 1, wherein the analysis component is further configuredto perform the analysis based on a correlation of the filtered data withextrinsic data collected from one or more sources that are external tothe industrial enterprise.
 7. The system of claim 6, wherein theextrinsic data is at least one of energy cost data, material cost data,material availability data, transportation schedule data, marketindicator data, web site traffic statistics, security information dataidentifying current information security breaches, or health statisticdata.
 8. The system of claim 6, wherein the analysis component isfurther configured to predict, based on another result of the analysisand a subset of the extrinsic data, an increase in a demand for aproduct produced by the industrial process, and the communicationcomponent is configured to send report data to the client device oranother client device rendering information identifying the increase indemand.
 9. The system of claim 8, wherein the analysis component isfurther configured to make a determination that a current predictedinventory level for the product is insufficient to satisfy the demand,and the communication component is configured to, in response to thedetermination, send report data to the client device or another clientdevice that renders a recommendation to increase a production rate ofthe industrial process.
 10. The system of claim 1, wherein the processdata is further appended with information identifying one or more otherdevices communicatively connected to the industrial device and afunctional relationship between the industrial device and the one ormore other devices, and the analysis component is further configured toperform the analysis based on the functional relationship.
 11. A methodfor analyzing industrial data, comprising: receiving, at a cloudplatform by a system comprising at least one processor, data from anindustrial device of an industrial enterprise, wherein the datacomprises at least process data relating to an industrial processcontrolled at least in part by the industrial device appended with aplant site identifier identifying a plant site at which the industrialdevice is located, a production line identifier identifying a productionline associated with the industrial device, and device identificationdata identifying the industrial device; storing, by the system, the datain cloud-based storage with other data collected from other industrialdevices of other industrial enterprises; filtering, by the system, thedata and the other data in the cloud-based storage according to anindustry type and a type of industrial application defined on a customermodel to yield filtered data; identifying, by the system based on ananalysis on the filtered data, at least one of a predicted performanceissue of the industrial device or a configuration modification forimproving a performance metric of the industrial device; and in responseto the identifying, sending, by the system, output data to a clientdevice via the cloud platform, the output data identifying at least oneof the predicted performance issue or the configuration modification.12. The method of claim 11, further comprising: generating, by thesystem and based on another analysis of the filtered data, comparativeperformance metrics for different device configurations represented bythe filtered data; and sending, by the system, report data to the clientdevice or another client device rendering the comparative performancemetrics.
 13. The method of claim 11, further comprising: identifying, bythe system based on another analysis of the filtered data, a devicecompatibility issue between the industrial device and another industrialdevice; and sending, by the system, report data to the client device oranother client device rendering information about the devicecompatibility issue.
 14. The method of claim 11, further comprising:identifying, by the system and based on another analysis of the filtereddata, a correlation between downtime occurrences of machines thatperform the type of industrial application and a device firmware versionof industrial devices that at least partially control the machines, andsending, by the system, report data to the client device or anotherclient device identifying the correlation.
 15. The method of claim 11,wherein the identifying comprises performing the analysis on thefiltered data based on a correlation of the filtered data with extrinsicdata received from one or more sources that are external to theindustrial enterprise.
 16. The method of claim 15, further comprising:predicting, by the system based on the correlation of the filtered datawith the extrinsic data, an increase in a demand for a product producedby the industrial process, wherein the sending comprises sending anotification of the increase in the demand to the client device oranother client device.
 17. The method of claim 16, further comprising:determining, by the system and based on the predicting, that a currentpredicted inventory level for the product is insufficient to satisfy thedemand, and in response to the determining that the current predictedinventory level is insufficient to satisfy the demand, including, in thenotification, a recommendation to increase a production rate of theindustrial process.
 18. The method of claim 11, wherein the receivingcomprises receiving the process data further appended with informationidentifying one or more other devices communicatively connected to theindustrial device and a functional relationship between the industrialdevice and the one or more other devices, and the identifying comprisesidentifying at least one of the predicted performance issue or theconfiguration modification based on the functional relationship.
 19. Anon-transitory computer-readable medium having stored thereoncomputer-executable instructions that, in response to execution, cause acomputing system to perform operations, the operations comprising:receiving data from an industrial device of an industrial enterprise viaa cloud interface and storing the data on one or more cloud storagedevices of a cloud platform, wherein the data comprises at leastproduction data associated with an industrial process controlled in partby the industrial device, plant site identifier identifying a plant siteat which the industrial device is located, a production line identifieridentifying a production line associated with the industrial device, anddevice identification data identifying the industrial device;aggregating the data with other data received from other industrialdevices of other industrial enterprises; filtering the data and theother data in the cloud storage devices according to an industry typeand a type of industrial application defined on a customer model toyield filtered data; determining, based on an analysis performed on thefiltered data, at least one of a predicted performance issue of theindustrial device or a configuration modification for improving aperformance metric of the industrial device; and in response to thedetermining, sending output data to a client device via the cloudplatform, the output data identifying at least one of the predictedperformance issue or the configuration modification.
 20. Thenon-transitory computer-readable medium of claim 19, further comprising:generating, based on another analysis performed on the filtered data,comparative performance metrics for different device configurationsrepresented by the filtered data; and sending, to the client device oranother client device, report data rendering the comparative performancemetrics.